{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T05:00:00Z","timestamp":1648702800327},"reference-count":0,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Unc. Fuzz. Knowl. Based Syst."],"published-print":{"date-parts":[[1994,9]]},"abstract":"<jats:p> In this paper, the hysteresis characterization in fuzzy spaces is presented by utilizing a fuzzy learning algorithm to generate fuzzy rules automatically from numerical data. The hysteresis phenomenon is first described to analyze its underlying mechanism. Then a fuzzy learning algorithm is presented to learn the hysteresis phenomenon and is used for predicting a simple hysteresis phenomenon. The results of learning are illustrated by mesh plots and input-output relation plots. Furthermore, the dependency of prediction accuracy on the number of fuzzy sets is studied. The method provides a useful tool to model the hysteresis phenomenon in fuzzy spaces. <\/jats:p>","DOI":"10.1142\/s0218488594000298","type":"journal-article","created":{"date-parts":[[2004,11,18]],"date-time":"2004-11-18T21:21:13Z","timestamp":1100812873000},"page":"351-360","source":"Crossref","is-referenced-by-count":0,"title":["HYSTERESIS CHARACTERIZATION BY A FUZZY LEARNING ALGORITHM"],"prefix":"10.1142","volume":"02","author":[{"given":"EDGE C.","family":"YEH","sequence":"first","affiliation":[{"name":"Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan, China"}]},{"given":"SHAO HOW","family":"LU","sequence":"additional","affiliation":[{"name":"Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan, China"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"container-title":["International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218488594000298","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T22:53:22Z","timestamp":1565132002000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218488594000298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1994,9]]},"references-count":0,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[1994,9]]}},"alternative-id":["10.1142\/S0218488594000298"],"URL":"https:\/\/doi.org\/10.1142\/s0218488594000298","relation":{},"ISSN":["0218-4885","1793-6411"],"issn-type":[{"value":"0218-4885","type":"print"},{"value":"1793-6411","type":"electronic"}],"subject":[],"published":{"date-parts":[[1994,9]]}}}